Modelling the long-run learning impact of the Covid-19 learning shock: Actions to (more than) mitigate loss

This paper uses a calibrated “pedagogical production function” model to estimate the potential long-term losses to children’s learning from the temporary shock of Covid-19 related school closures. It then models possible gains from two mitigation strategies. Without mitigation, children could lose more than a full year’s worth of learning from a three-month school closure because they will be behind the curriculum when they re-enter school and will fall further behind as time goes on. Remediation when children return to school reduces the long-term learning loss by half, but still leaves children more than half a year behind where they would have been with no shock. Remediation combined with long-term reorientation of curriculum to align with children’s learning levels fully mitigates the long-term learning loss due to the shock and surpasses the learning in the counterfactual of no shock by more than a full year’s worth of learning. Systems need to begin planning now for remediation programmes, and as they do so they should build programmes and train teachers in ways that can continue to produce benefits beyond the period immediately following reopening.     

Should Children Attend Their Parent's Funerals?

The paper discusses elements of the findings of 'Iceberg', a doctoral study at the University of York addressing many issues of child bereavement through a retrospective study of nearly one hundred individuals who had either experienced the death of a parent when they were at school, or were the surviving parent of such a child. The research revealed issues of lack of control and information for children after a parental death. Forty-seven per cent of those in the study attended the funeral of their parent. There was correlation between the age of children and attendance at the funeral. Of the fifty-three per cent of children not attending, twenty-four per cent of them were forbidden to attend, whilst eleven per cent of them were distracted from attending the funeral, such as being sent to school or a neighbours and being unaware that it was taking place. None of the children attending the funeral reported any negative experiences, and two thirds of them reported it as a positive or helpful event. In contrast, over three-quarters of those not attending later wished that they had. Over a third had feelings of regret, others feeling that they had been excluded from the family at the time of the death, and felt anger, hurt, and frustration. The conclusion was that the best strategy is to give children informed choice about whether or not to attend. If children do attend a negative outcome seems unlikely, but it would be prudent to prepare them as to what to expect at the ceremony.     

力量锻炼在控制体重中的作用机理初探

力量与力量耐力是健康体适能的重要组成成分。研究表明，力量锻炼在控制体重中的作用机理主要是通过以下两种途径实现的：一是增加安静时的能量代谢率；二是增加脂肪氧化。建议人们，要获得最佳健康并有效控制体重，应将有氧运动与力量锻炼相结合。     

关注教师的教育情感

作者对目前教师教育情感的失落作了简要描述,以期引起重视。     

基于模糊语义距离的多媒体信息检索方法研究

与传统的数据库精确查询不同 ,多媒体信息检索的查询条件是不完备的。本文叙述利用模糊语义距离来检索多媒体数据库中信息的原理、算法 ,并将模糊相似测试作为检索结果判断标准 ,最后通过一个示例来说明本方法的使用。     

运动减肥机制探析———酰化刺激蛋白的作用及其调节

酰化刺激蛋白是脂肪组织分泌的一种激素，在调节脂肪代谢和能量平衡方面发挥重要作用。它一方面能够提高脂肪细胞捕获脂肪酸和合成甘油三酯的能力；另一方面可以通过抑制激素敏感脂酶的活性来抑制甘油三酯的分解。C3（-/-）（ASP缺乏）鼠所表现出的肥胖抵抗特征对于防肥减肥而言具有诱人的前景。综述了ASP的来源、生物学作用以及缺乏后的生物学特征，并对其在运动减肥机制中可能发挥的作用进行探讨。     

赛前减体重对运动员运动能力的影响

赛前减体重能够对运动员的运动能力、身体机能和营养健康造成影响。笔者主要综述了国内外对减体重的研究资料,详细的对这一影响进行了概述,并认为在减体重时应用身体成分、蛋白质、无机盐和维生素代谢以及身体免疫能力等数个指标进行科学检测。     

中文自动问答中旬子相似度计算研究

句子相似度计算是自动问答系统的重要理论基础和关键实现技术.目前,用于中文自动问答系统的句子相似度计算方法很多,由于缺乏系统的分析,给研究人员带来了较大的不便.依据所利用的特征信息,可以将这些方法分为四类,即基于关键词信息、基于语义信息、基于句法结构信息以及基于多重信息.通过对各类方法实验结果的比较,指出各自的优势和不足.同时指出,基于多重信息的方法是当前的主流方法,实现不同特征信息的最佳权重分配是该类方法今后的研究重点.另外,还提出一个有关相似度概念认识上的看法,即对于中文自动问答系统,实质上依据的是句子的相关度,而不是句子的相似度.通过本文的研究,旨在为中文自动问答领域的句子相似度计算研究提供一定的参考.     

基于递归概念图的文本检索模型研究

Sowa在1984年提出了一个抽象模型,即概念图,作为基于语言学、心理学和哲学的知识表示语言.但是其概念图的定义比较简单,对于刻画文本中的语言组块的语义及语言单元之间的组合运算比较困难.为了能从形式上为将来的组块内部和组块之间的组合分析提供支持,深化概念图的表示形式,我们提出了一种适用于自然语言自动化分析的概念图的形式化描述,它可以看成是Sowa 概念图的递归扩展.然后我们将这种新颖的知识表示方式应用到文本检索领域的文本标引中,同时给出了以此为基础的概念图匹配算法.在和布尔检索的比较实验中,概念检索体现出比较明显的优势.     

基于语义的半结构化P2P网络

如何利用具有本体标注的结构化文档中的语义信息组织P2P网络,提供对基于语义的信息共享与查询的P2P网络支持,是当前P2P网络的研究热点之一.本文提出采用Peer所存储文档中的加权本体概念向量作为Peer的特征向量,通过相似度计算将Peer聚成Peer组,从而构造基于语义的半结构化P2P网络.用户的查询请求由各Peer组内的组服务器负责路由转发,组服务器计算查询请求与各路由表项之间的相似度,将查询向最有可能包含查询目标的Peer组转发.文中较全面地阐述了本体概念局部和全局权重的计算方法.由于P2P网络拓扑的建立过程和查询路由过程均基于语义信息,使得网络的各项性能与基于关键字处理的P2P网络相比,得到了较大的提高.